Name
Abstract | ||
|---|---|---|
Pattern matching is the most central task for text indices. Most recent indices leverage compression techniques to make pattern matching feasible for massive but highly-compressible datasets. Within this kind of indices, we propose a new compressed text index built upon a grammar compression based on induced suffix sorting [Nunes et al., DCC'18]. We show that this grammar exhibits a locality sensitive parsing property, which allows us to specify, given a pattern $P$, certain substrings of $P$, called cores, that are similarly parsed in the text grammar whenever these occurrences are extensible to occurrences of $P$. Supported by the cores, given a pattern of length $m$, we can locate all its $occ$ occurrences in a text $T$ of length $n$ within $O(m \lg |\mathcal{S}| + occ_C \lg|\mathcal{S}| \lg n + occ)$ time, where $\mathcal{S}$ is the set of all characters and non-terminals, $occ$ is the number of occurrences, and $occ_C$ is the number of occurrences of a chosen core $C$ of $P$ in the right hand side of all production rules of the grammar of $T$. Our grammar index requires $O(g)$ words of space and can be built in $O(n)$ time using $O(g)$ working space, where $g$ is the sum of the right hand sides of all production rules. We underline the strength of our grammar index with an exhaustive practical evaluation that gives evidence that our proposed solution excels at locating long patterns in highly-repetitive texts. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86692-1_8 | SPIRE |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tooru Akagi | 1 | 1 | 0.70 |
| Dominik Köppl | 2 | 30 | 10.31 |
| Yuto Nakashima | 3 | 57 | 19.52 |
| Shunsuke Inenaga | 4 | 595 | 79.02 |
| Hideo Bannai | 5 | 620 | 79.87 |
| Masayuki Takeda | 6 | 902 | 79.24 |